System and method for generating a pulmonary dysfunction nourishment program

ABSTRACT

A system and method for generating a pulmonary dysfunction nourishment program, comprises a computing device configured to receive at least a respiratory volume collection relating to a user, generate at least a respiratory parameter of a plurality of respiratory parameter as a function of the respiratory volume collection, determine a pulmonary bundle element as a function of the at least respiratory parameter, identify at least an edible as a function of the pulmonary bundle element, wherein identifying comprises, obtaining a nourishment composition from an edible directory, determining a nourishment deficiency as a function of the pulmonary bundle element, and identifying the at least edible as a function of the nourishment composition, the nourishment deficiency, and an edible machine-learning model, and output a nourishment program as a function of the at least edible.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed to asystem and method for generating a pulmonary dysfunction nourishmentprogram.

BACKGROUND

Current edible suggestion systems do not account for pulmonarycharacteristics of an individual. This leads to inefficiency of anedible suggestion system and a poor nutrition plan for the individual.This is further complicated by a lack of uniformity of nutritionalplans, which results in dissatisfaction of individuals.

SUMMARY OF THE DISCLOSURE

In an aspect a system for generating a pulmonary dysfunction nourishmentprogram, the system comprising a computing device, the computing deviceconfigured to receive at least a respiratory volume collection relatingto a user, generate at least a respiratory parameter of a plurality ofrespiratory parameter as a function of the respiratory volumecollection, determine a pulmonary bundle element as a function of the atleast respiratory parameter, identify at least an edible as a functionof the pulmonary bundle element, wherein identifying comprises,obtaining a nourishment composition from an edible directory,determining a nourishment deficiency as a function of the pulmonarybundle element, and identifying the at least edible as a function of thenourishment composition, the nourishment deficiency, and an ediblemachine-learning model, and output a nourishment program as a functionof the at least edible.

In another aspect a method for generating a pulmonary dysfunctionnourishment program, the method comprising receiving, by a computingdevice, at least a respiratory volume collection relating to a user,generating, by the computing device, at least a respiratory parameter ofa plurality of respiratory parameter as a function of the respiratoryvolume collection, determining, by the computing device, a pulmonarybundle element as a function of the at least respiratory parameter,identifying, by the computing device, at least an edible as a functionof the pulmonary bundle element, wherein identifying comprises,obtaining a nourishment composition from an edible directory,determining a nourishment deficiency as a function of the pulmonarybundle element, and identifying the at least edible as a function of thenourishment composition, the nourishment deficiency, and an ediblemachine-learning model, and outputting, by the computing device, anourishment program as a function of the at least edible.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for generating a pulmonary dysfunction nourishment program;

FIG. 2 is a representative diagram of an exemplary embodiment ofrespiratory parameters according to an embodiment of the invention;

FIG. 3 is a block diagram of an exemplary embodiment of an edibledirectory according to an embodiment of the invention;

FIG. 4 is a representative diagram of an exemplary embodiment ofbiomarkers that can be received from a respiratory volume collectionaccording to an embodiment of the invention;

FIG. 5 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 6 is a process flow diagram illustrating an exemplary embodiment ofa method of generating a pulmonary dysfunction nourishment program; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a pulmonary dysfunction nourishmentprogram. In an embodiment, the disclosure may receive at least arespiratory volume relating to a user. Aspects of the present disclosurecan be used to generate at least a respiratory parameter as a functionof the respiratory volume collection using respiratory algorithms.Aspects of the present disclosure can also be used to determine apulmonary bundle as a function of the respiratory parameter. Aspects ofthe present disclosure can be used to identify at least an edible as afunction of the pulmonary bundle element. This is so, at least in part,because disclosure utilizes an edible machine-learning model. Aspects ofthe present disclosure allow for outputting a nourishment program.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forgenerating a pulmonary dysfunction nourishment program is illustrated.System includes a computing device 104. Computing device 104 may includeany computing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Computing device104 may include a single computing device operating independently, ormay include two or more computing device operating in concert, inparallel, sequentially or the like; two or more computing devices may beincluded together in a single computing device or in two or morecomputing devices. Computing device 104 may interface or communicatewith one or more additional devices as described below in further detailvia a network interface device. Network interface device may be utilizedfor connecting computing device 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Computing device 104 mayinclude but is not limited to, for example, a computing device orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. Computingdevice 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Computing device 104 may distribute one or more computing tasks asdescribed below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. Computing device 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Still referring to FIG. 1, computing device 104 receives at least arespiratory volume collection 108 relating to a user. As used in thisdisclosure “respiratory volume collection” is a volume of biologicalsample from an individual associated with the respiratory system of anindividual. A biological sample may include, without limitation,exhalate, blood, sputum, urine, saliva, feces, semen and other bodilyfluids, as well as tissue. Exhalate may include, without limitation,breath that is expelled from an individual via breathing and/or someother forced exhalation of breath from an individual. As a non-limitingexample respiratory volume collection 108 may include a volume of 2.5 Lof breath exhaled from an individual. As a further non-limiting example,respiratory volume collection 108 may include a volume of 0.1 L of bloodfrom the pulmonary system of an individual. Respiratory volumecollection 108 may include expired breath from any pulmonary entryand/or exit pathway, including but not limited to oral and/or nasalpassages. Respiratory volume collection 108 may be received as afunction of obtaining a respiratory signal from at least a sensor. Asused in this disclosure “respiratory signal” is datum that relates toand/or represents an element associated with the status of anindividual's respiratory system. As a non-limiting example a respiratorysignal may include an image of a lung of an individual from a magneticresonance imaging medical device. As a further non-limiting example arespiratory signal may include one or more lights, voltages, currents,sounds, chemicals, pressures, moistures, and the like thereof.Respiratory signal may include one or more respiratory biomarkersassociated with the pulmonary system of an individual, wherein abiomarker is one or more chemicals, components, molecules, gases, andthe like there of as described in detail below, in reference to FIG. 4.As used in this disclosure “sensor” is a device that records, monitors,stores, measures, and/or transmits respiratory signals. As anon-limiting example, a sensor may include an imaging sensor, such asoptical cameras, infrared cameras, 3D cameras, multispectral cameras,hyperspectral cameras, polarized cameras, chemical sensors, motionsensors, ranging sensors, light radar component, such as lidar,detection or imaging using radio frequencies component, such as radar,terahertz or millimeter wave imagers, seismic sensors, magnetic sensors,weight/mass sensors, ionizing radiation sensors, and/or acousticalsensors. As a further non-limiting example, a sensor may include one ormore medical devices that at least detect and/or monitor an individual'srespiratory system, such as semi-auto analyzers, photo colorimeters,cell photo colorimeters, hemoglobin meters, mass spectrometers,chromatographic instruments, and the like thereof.

Still referring to FIG. 1, computing device 104 generates at least arespiratory parameter 112 of a plurality of respiratory parameters as afunction of respiratory volume collection 108. As used in thisdisclosure “respiratory parameter” is a measurable value associated withan individual's respiratory system. As a non-limiting examplerespiratory parameter may include one or more chemical concentrations,rates of flow, lung volumes, diffusion capacities, and the like thereof. As a further non-limiting example a respiratory parameter mayinclude, without limitation, an inspiratory reserve volume (IRV), tidalvolume (TV), expiratory reserve volume (ERV), residual volume (RV),inspiratory capacity (IC), functional residual capacity (FRC), vitalcapacity (VC), total lung capacity (TLC), as described in detail below,in reference to FIG. 2. Respiratory parameter 112 is generated as afunction of a respiratory algorithm 116. As used in this disclosure“respiratory algorithm” is an algorithm that determines one or morerespiratory measurements. As a non-limiting example, respiratoryalgorithm 116 may include algorithms such as a minute ventilation,alveolar minute ventilation, airway resistance, mean airway pressure,work of breathing, alveolar-arterial oxygen tension gradient, alveolaroxygen tension, arterial/alveolar oxygen tension, arterial oxygencontent, end-capillary oxygen content, mixed venous oxygen content,shunt equation, modified shunt equation, arterial-mixed venous oxygencontent difference, oxygen-to-air entrainment ratio, arterial oxygensaturation estimation, P/F ratio, oxygenation index, oxygen consumption,oxygen extraction ratio, fiO2 estimation for nasal cannula, oxygencylinder duration, liquid oxygen system duration, cardiac index, cardiacoutput, cardiac output Fick's method, cerebral perfusion pressure, meanarterial pressure, stroke volume, maximum heart rate, heart rate on anEKG strip, respiratory quotient, systemic vascular resistance, pulmonaryvascular resistance, static compliance, dynamic compliance, dead spaceto tidal volume ratio, children dosage estimation, infant dosageestimation, infant and children dosage estimation, anion gap, bodysurface area elastance, smoking use calculation, suction catheter sizeestimation, endotracheal tube size estimation in children, Boyle's law,Charles' law, Gay-Lussac's law, LaPlace's law, Celsius to Fahrenheittemperature conversion, Fahrenheit to Celsius temperature conversion,Celsius to Kelvin temperature conversion, helium/oxygen conversion,total lung capacity, pressure support ventilator setting, rapid shallowbreathing index, endotracheal tube size estimation in children, minimumflow rate in mechanical ventilation, and the like thereof.

Still referring to FIG. 1, computing device 104 determines a pulmonarybundle element 120 as a function of respiratory parameter 116. As usedin this disclosure “pulmonary bundle element” is a profile of a user'srespiratory status consisting of a group of respiratory parameters. As anon-limiting example pulmonary bundle element 120 may group respiratoryparameters of oxygen saturation, cardiac index, tidal volume, and oxygencylinder duration. Computing device 104 may determine pulmonary bundleelement 120 by identifying at least a pulmonary deficiency as a functionof the respiratory parameter. As used in this disclosure “pulmonarydeficiency” is an inadequacy and/or deficiency of a respiratoryparameter. As a non-limiting example a pulmonary deficiency may existdue to a respiratory rate of 20, wherein a respiratory rate should be 40according to a respiratory threshold. As used in this disclosure“respiratory threshold” is a threshold a respiratory parameter shouldbe. Respiratory threshold may be identified according to one or moremedical guidelines for the measurement of respiratory function. As anon-limiting example a medical guideline for the measurement ofrespiratory function may include a defined threshold according to theAmerican Association for Respiratory Care, American Medical Association,American College of Physicians, and the like thereof. As a furthernon-limiting example, a medical guideline for the measurement ofrespiratory function may include a defined threshold according to one ormore medical research journals, such as the Lancet, New England Journalof Medicine, Science, Journal of the American Medical Association, andthe like thereof.

Still referring to FIG. 1, computing device 104 identifies at least anedible as a function of pulmonary bundle element 120. As used in thisdisclosure an “edible” is a source of nourishment that may be consumedby a user such that the user may absorb the nutrients from the source.For example and without limitation, an edible may include legumes,plants, fungi, nuts, seeds, breads, dairy, eggs, meat, cereals, rice,seafood, desserts, dried foods, dumplings, pies, noodles, salads, stews,soups, sauces, sandwiches, and the like thereof. Computing device 104identifies edible 124 as a function of obtaining a nourishmentcomposition 128. As used in this disclosure “nourishment composition” isa list and/or compilation of all of the nutrients contained in anedible. As a non-limiting example nourishment composition 128 mayinclude one or more quantities and/or amounts of total fat, includingsaturated fat and/or trans-fat, cholesterol, sodium, totalcarbohydrates, including dietary fiber and/or total sugars, protein,vitamin A, vitamin C, thiamin, riboflavin, niacin, pantothenic acid,vitamin b6, folate, biotin, vitamin B12, vitamin D, vitamin E, vitaminK, calcium, iron, phosphorous, iodine, magnesium, zinc, selenium,copper, manganese, chromium, molybdenum, chloride, and the like thereof.Nourishment composition 128 may be obtained as a function of an edibledirectory 132, wherein an edible directory is a database of edibles thatmay be identified as a function of one or more pulmonary bundleelements, as described in detail below, in reference to FIG. 3.Computing device 104 determines a nourishment deficiency 136 as afunction of pulmonary bundle element 120. As used in this disclosure“nourishment deficiency” is an inadequacy and/or deficiency of anutrient in a user's body. As a non-limiting example pulmonary bundleelement 120 may determine a reduced hemoglobin concentration, wherein anourishment deficiency may be identified as low iron. Nourishmentdeficiency 136 may be identified according to one or more nourishmentguidelines. As a non-limiting example a nourishment guideline may beidentified according to a peer-review research journal, such as theJournal of Nutrition, Nutrition and Health, Advances in Nutrition, andthe like thereof.

Still referring to FIG. 1, computing device 104 identifies edible 124 asa function of nourishment composition 128, nourishment deficiency 136,and an edible machine-learning model 140. As used in this disclosure“edible machine-learning model” is a machine-learning model to producean edible output given nourishment compositions and nourishmentdeficiencies as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Ediblemachine-learning model 140 may include one or more ediblemachine-learning processes such as supervised, unsupervised, orreinforcement machine-learning processes that computing device 104and/or a remote device may or may not use in the determination of edible124. As used in this disclosure “remote device” is an external device tocomputing device 104. An edible machine-learning process may include,without limitation machine learning processes such as simple linearregression, multiple linear regression, polynomial regression, supportvector regression, ridge regression, lasso regression, elasticnetregression, decision tree regression, random forest regression, logisticregression, logistic classification, K-nearest neighbors, support vectormachines, kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

Still referring to FIG. 1, computing device 104 may train ediblemachine-learning process as a function of an edible training set. Asused in this disclosure a “edible training set” is a training set thatcorrelates at least nourishment composition and nourishment deficiencyto an edible. For example, and without limitation, nourishmentcomposition of 14 g of protein and 2 g of fiber and a nourishmentdeficiency of low levels of protein CC16 as a function of chronicobstructive pulmonary disease may relate to an edible of salmon. Theedible training set may be received as a function of user-enteredvaluations of nourishment compositions, nourishment deficiencies, and/oredibles. Computing device 104 may receive edible training set byreceiving correlations of nourishment compositions and/or nourishmentdeficiencies that were previously received and/or determined during aprevious iteration of determining edibles. The edible training set maybe received by one or more remote devices that at least correlate anourishment composition and nourishment deficiency to an edible, whereina remote device is an external device to computing device 104, asdescribed above.

Still referring to FIG. 1, edible machine-learning model 140 mayidentify edible 120 as a function of one or more classifiers. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Computing device 104 and/or another devicemay generate a classifier using a classification algorithm, defined as aprocesses whereby a computing device 104 derives a classifier fromtraining data. Classification may be performed using, withoutlimitation, linear classifiers such as without limitation logisticregression and/or naive Bayes classifiers, nearest neighbor classifierssuch as k-nearest neighbors classifiers, support vector machines, leastsquares support vector machines, fisher's linear discriminant, quadraticclassifiers, decision trees, boosted trees, random forest classifiers,learning vector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1, computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1, computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast one value. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm:

${l = \sqrt{\sum\limits_{i = 0}^{n}\; a_{i}^{2}}},$where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Still referring to FIG. 1, computing device 104 may receive ediblemachine-learning model 140 from the remote device that utilizes one ormore edible machine learning processes, wherein a remote device isdescribed above in detail. For example, and without limitation, a remotedevice may include a computing device, external device, processor, andthe like thereof. The remote device may perform the ediblemachine-learning process using the edible training set to generateedible 124 and transmit the output to computing device 104. The remotedevice may transmit a signal, bit, datum, or parameter to computingdevice 104 that at least relates to edible 124. Additionally oralternatively, the remote device may provide an updated machine-learningmodel. For example, and without limitation, an updated machine-learningmodel may be comprised of a firmware update, a software update, anedible machine-learning process correction, and the like thereof. As anon-limiting example a software update may incorporate a new nourishmentcomposition that relates to a modified nourishment deficiency.Additionally or alternatively, the updated machine learning model may betransmitted to the remote device, wherein the remote device may replacethe edible machine-learning model with the updated machine-learningmodel and determine the edible as a function of the nourishmentdeficiency using the updated machine-learning model. The updatedmachine-learning model may be transmitted by the remote device andreceived by computing device 104 as a software update, firmware update,or corrected edible machine-learning model. For example, and withoutlimitation an edible machine-learning model may utilize a neural netmachine-learning process, wherein the updated machine-learning model mayincorporate polynomial regression machine-learning process. Updatedmachine learning model may additionally or alternatively include anymachine-learning model used as an updated machine learning model asdescribed in U.S. Nonprovisional application Ser. No. 17/106,658, filedon Nov. 30, 2020, and entitled “A SYSTEM AND METHOD FOR GENERATING ADYNAMIC WEIGHTED COMBINATION,” the entirety of which is incorporatedherein by reference.

Still referring to FIG. 1, computing device 104 may identify edible as afunction of determining a pulmonary dysfunction. As used in thisdisclosure “pulmonary dysfunction” is an ailment and/or collection ofailments that impact an individual's respiratory system. As anon-limiting example, pulmonary dysfunctions may include asthma, chronicobstructive pulmonary disorder, chronic bronchitis, emphysema, lungcancer, cystic fibrosis, pneumonia, pleural effusion, acute bronchitis,pulmonary edema, sarcoidosis, asbestosis, autoimmune pulmonary alveolarproteinosis, Blau syndrome, bronchogenic cysts, Cantu syndrome, Gaucherdisease, Henoch-Schonlein purpura, idiopathic pulmonary fibrosis, andthe like thereof. Pulmonary dysfunction may be determined as a functionof one or more pulmonary machine-learning models. As used in thisdisclosure “pulmonary machine-learning model” is a machine-learningmodel to produce a pulmonary dysfunction output given pulmonary bundleelements as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Pulmonarymachine-learning model may include one or more pulmonarymachine-learning processes such as supervised, unsupervised, orreinforcement machine-learning processes that computing device 104and/or a remote device may or may not use in the determination ofpulmonary dysfunction. As used in this disclosure “remote device” is anexternal device to computing device 104. A pulmonary machine-learningprocess may include, without limitation machine learning processes suchas simple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

Still referring to FIG. 1, computing device 104 may train pulmonarymachine-learning process as a function of a pulmonary training set. Asused in this disclosure a “pulmonary training set” is a training setthat correlates at least ventilatory enumeration and ventilatory effectto a pulmonary dysfunction. As used in this disclosure “ventilatoryenumeration” is a measurable value associated with a ventilatory systemand/or respiratory system. As used in this disclosure “ventilatoryeffect” is an impact and/or effect on the pulmonary system of anindividual. As a non-limiting example a ventilatory enumeration of 20may be established for a ventilatory effect of shortness of breath,wherein a pulmonary dysfunction of COVID-19 may be determined. Thepulmonary training set may be received as a function of user-enteredvaluations of ventilatory enumerations, ventilatory effects, and/orpulmonary dysfunctions. Computing device 104 may receive pulmonarytraining by receiving correlations of ventilatory enumerations and/orventilatory effects that were previously received and/or determinedduring a previous iteration of determining pulmonary dysfunction. Thepulmonary training set may be received by one or more remote devicesthat correlate a ventilatory enumeration and/or ventilatory effect to apulmonary dysfunction, wherein a remote device is an external device tocomputing device 104, as described above.

Still referring to FIG. 1, computing device 104 may receive pulmonarymachine-learning model from the remote device that utilizes one or morepulmonary machine learning processes, wherein a remote device isdescribed above in detail. For example, and without limitation, a remotedevice may include a computing device, external device, processor, andthe like thereof. The remote device may perform the pulmonarymachine-learning process using the pulmonary training set to generatepulmonary dysfunction and transmit the output to computing device 104.The remote device may transmit a signal, bit, datum, or parameter tocomputing device 104 that at least relates to pulmonary dysfunction.Additionally or alternatively, the remote device may provide an updatedmachine-learning model. For example, and without limitation, an updatedmachine-learning model may be comprised of a firmware update, a softwareupdate, a pulmonary machine-learning process correction, and the likethereof. As a non-limiting example a software update may incorporate anew ventilatory enumeration that relates to a modified ventilatoryeffect. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device, wherein the remote devicemay replace the pulmonary machine-learning model with the updatedmachine-learning model and determine the pulmonary dysfunction as afunction of the ventilatory enumeration using the updatedmachine-learning model. The updated machine-learning model may betransmitted by the remote device and received by computing device 104 asa software update, firmware update, or corrected pulmonarymachine-learning model. For example, and without limitation pulmonarymachine-learning model may utilize a logistic regressionmachine-learning process, wherein the updated machine-learning model mayincorporate decision tree machine-learning process.

Still referring to FIG. 1, computing device 104 may identify edible as afunction of a likelihood parameter. As used in this disclosure“likelihood parameter” is a parameter that identities the probability ofa user to consume an edible. As a non-limiting example likelihoodparameter may identify a high probability that a user will consume anedible of steak. As a further non-limiting example likelihood parametermay identify a low probability that a user will consume an edible ofcookies. Likelihood parameter may be determined as a function of a usertaste profile. As used in this disclosure “user taste profile” is aprofile of a user that identifies one or more desires, preferences,wishes, and/or wants that a user has. As a non-limiting example a usertaste profile may include a user's preference for chicken flavor and/orcrunchy textured edibles. Likelihood parameter may be determined as afunction of an edible profile. As used in this disclosure “edibleprofile” is taste of an edible is the sensation of flavor perceived inthe mouth and throat on contact with the edible. Edible profile mayinclude one or more flavor variables. As used in this disclosure “flavorvariable” is a variable associated with the distinctive taste of anedible, wherein a distinctive may include, without limitation sweet,bitter, sour, salty, umami, cool, and/or hot. Edible profile may bedetermined as a function of receiving flavor variable from a flavordirectory. As used in this disclosure “flavor directory” is a databaseof flavors for an edible. As a non-limiting example flavor directory mayinclude a list and/or collection of edibles that all contain umamiflavor variables. As a further non-limiting example flavor directory mayinclude a list and/or collection of edibles that all contain sour flavorvariables. Likelihood parameter may alternatively or additionallyinclude any user taste profile and/or edible profile used as alikelihood parameter as described in U.S. Nonprovisional applicationSer. No. 17/032,080, filed on Sep. 25, 2020, and entitled “METHODS,SYSTEMS, AND DEVICES FOR GENERATING A REFRESHMENT INSTRUCTION SET BASEDON INDIVIDUAL PREFERENCES,” the entirety of which is incorporated hereinby reference.

Still referring to FIG. 1, computing device 104 outputs a nourishmentprogram 144 of a plurality of nourishment programs as a function of theedible 124. As used in this disclosure “nourishment program” is aprogram consisting of one or more edibles that are to be consumed over agiven time period, wherein a time period is a temporal measurement suchas seconds, minutes, hours, days, weeks, months, years, and the likethereof. As a non-limiting example nourishment program 144 may consistof recommending steak for 3 days. As a further non-limiting examplenourishment program 144 may recommend chicken for a first day, spaghettifor a second day, and mushrooms for a third day. Nourishment program 144may include one or more diet programs such as paleo, keto, vegan,vegetarian, and the like thereof. Nourishment program 144 may beoutputted as a function an intended outcome. As used in this disclosure“intended outcome” is an outcome that an edible may generate accordingto a predicted and/or purposeful plan. As a non-limiting example,intended outcome may include a treatment outcome. As used in thisdisclosure “treatment outcome” is an intended outcome that is designedto at least reverse and/or eliminate the effects of the pulmonary bundleelement and/or pulmonary dysfunction. As a non-limiting example, atreatment outcome may include reversing the effects of emphysema. As afurther non-limiting example, a treatment outcome includes reversing thepulmonary dysfunction of lung fibrosis. Intended outcome may include aprevention outcome. As used in this disclosure “prevention outcome” isan intended outcome that is designed to at least prevent and/or avert apulmonary bundle element and/or pulmonary dysfunction. As a non-limitingexample, a prevention outcome may include preventing the development ofchronic obstructive pulmonary disease.

Still referring to FIG. 1, computing device 104 may output nourishmentprogram 144 as a function of the intended outcome using a nourishmentmachine-learning model. As used in this disclosure “nourishmentmachine-learning model” is a machine-learning model to produce anourishment program output given edibles and/or intended outcomes asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language. Nourishment machine-learningmodel may include one or more nourishment machine-learning processessuch as supervised, unsupervised, or reinforcement machine-learningprocesses that computing device 104 and/or a remote device may or maynot use in the output of nourishment program 144. As used in thisdisclosure “remote device” is an external device to computing device104. Nourishment machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

Still referring to FIG. 1, computing device 104 may train nourishmentmachine-learning process as a function of a nourishment training set. Asused in this disclosure a “nourishment training set” is a training setthat correlates an intended outcome to an edible. The nourishmenttraining set may be received as a function of user-entered edibles,intendent outcomes, and/or nourishment programs. Computing device 104may receive nourishment training by receiving correlations of intendedoutcomes and/or edibles that were previously received and/or determinedduring a previous iteration of outputted nourishment programs. Thenourishment training set may be received by one or more remote devicesthat correlate an intended outcome and/or edible to a nourishmentprogram, wherein a remote device is an external device to computingdevice 104, as described above.

Still referring to FIG. 1, computing device 104 may receive nourishmentmachine-learning model from the remote device that utilizes one or morenourishment machine learning processes, wherein a remote device isdescribed above in detail. For example, and without limitation, a remotedevice may include a computing device, external device, processor, andthe like thereof. The remote device may perform the nourishmentmachine-learning process using the nourishment training set to outputnourishment program 144 and transmit the output to computing device 104.The remote device may transmit a signal, bit, datum, or parameter tocomputing device 104 that at least relates to nourishment program 144.Additionally or alternatively, the remote device may provide an updatedmachine-learning model. For example, and without limitation, an updatedmachine-learning model may be comprised of a firmware update, a softwareupdate, a nourishment machine-learning process correction, and the likethereof. As a non-limiting example a software update may incorporate anew intended outcome that relates to a modified edible. Additionally oralternatively, the updated machine learning model may be transmitted tothe remote device, wherein the remote device may replace the nourishmentmachine-learning model with the updated machine-learning model andoutput the nourishment program as a function of the intended outcomeusing the updated machine-learning model. The updated machine-learningmodel may be transmitted by the remote device and received by computingdevice 104 as a software update, firmware update, or correctednourishment machine-learning model. For example, and without limitationnourishment machine-learning model may utilize a nearest neighbormachine-learning process, wherein the updated machine-learning model mayincorporate association rules machine-learning processes.

Now referring to FIG. 2, an exemplary embodiment 200 of a representativediagram of respiratory parameter 112 according to an embodiment of theinvention is illustrated. Respiratory parameter 112 may include one ormore respiratory measurements according to a breathing cycle 204. Asused in this disclosure “breathing cycle” is the movement of air duringboth inhalation and exhalation, wherein inhalation is represented as anincrease along a y-axis representing volume in mL/kg and exhalation isrepresented as a decreases along a x-axis representing time. Breathingcycle 204 may represent both regulatory breathing, wherein theindividual is not participating in any strenuous respiratory activitiesand/or strenuous breathing, wherein the individual is inhaling air in anattempt to maximize the inhalation and exhaling the maximum amount ofair that was inhaled. Respiratory parameter 112 may include aninspiratory reserve volume (IRV) 208 as a function of breathing cycle204. As used in this disclosure “inspiratory reserve volume (IRV)” isthe additional amount of air that can be inhaled after a normalinhalation. As a non-limiting example IRV 208 may include an individualthat may normally inhale 2 L of air, wherein an additional 1.5 L of airmay be additionally inhaled. Respiratory parameter 112 may include atidal volume (TV) 212 as a function of breathing cycle 204. As used inthis disclosure “tidal volume (TV)” is the amount of air that isinspired and expired during a normal breath. As a non-limiting example,TV 212 may include an individual that normally inhales and exhales 1 Lof air. Respiratory parameter 112 may include calculating an expiratoryreserve volume (ERV) 216 as a function of breathing cycle 204. As usedin this disclosure “expiratory reserve volume (ERV)” is the additionalamount of air that can be exhaled after a normal exhalation. As anon-limiting example, ERV 216 may include an individual that maynormally exhale 1 L of air, wherein an additional 2.5 L of air may beadditionally exhaled. Respiratory parameter 112 may include a residualvolume (RV) 220 as a function of breathing cycle 204. As used in thisdisclosure “residual volume (RV)” is the additional amount of air thatis left after ERV is exhaled. As a non-limiting example, RV 220 mayinclude an individual that has exhaled 2.5 L of ERV, wherein 1.25 L ofair remains in the lungs of the individual.

Still referring to FIG. 2, respiratory parameter 112 may includeinspiratory capacity (IC) 224 as a function of breathing cycle 204. Asused in this disclosure “inspiratory capacity (IC)” is the amount of airthat may be inhaled after the end of a normal expiration. As anon-limiting example IC 224 may include a total volume of 3.25 L thatmay be inhaled after an individual has normally exhaled. Respiratoryparameter 112 may include functional residual capacity (FRC) 228. Asused in this disclosure “functional residual capacity (FRC)” is theamount of additional air that can be exhaled after a normal exhalation.As a non-limiting example FRC 228 may include a total volume of 2.75 Lthat may be exhaled after an individual has normally inhaled.Respiratory parameter 112 may include vital capacity (VC) 232. As usedin this disclosure “vital capacity (VC)” is the maximum amount of airthat can be inhaled or exhaled during a respiratory cycle. As anon-limiting example VC 232 may the sum of ERV, TV, and IRV to determinea maximum amount of air that may be inhaled and/or exhaled by anindividual. Respiratory parameter 112 may include a total lung capacity(TLC) 236. As used in this disclosure “total lung capacity (TLC)” is thetotal amount of air that an individual's lung may hold. As anon-limiting example TLC 236 may the sum of RV, ERV, TV, and IRV todetermine a total amount of air that an individual's lung may hold.

Now referring to FIG. 3, an exemplary embodiment 300 of an edibledirectory 132 according to an embodiment of the invention isillustrated. Edible directory 132 may be implemented, withoutlimitation, as a relational databank, a key-value retrieval databanksuch as a NOSQL databank, or any other format or structure for use as adatabank that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Edible directory 132 mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Edible directory 132 may include a plurality of dataentries and/or records as described above. Data entries in a databankmay be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina databank may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistently with this disclosure. Edible directory 132 may include acarbohydrate tableset 304. Carbohydrate tableset 304 may relate to anourishment composition of an edible with respect to the quantity and/ortype of carbohydrates in the edible. As a non-limiting example,carbohydrate tableset 304 may include monosaccharides, disaccharides,oligosaccharides, polysaccharides, and the like thereof. Edibledirectory 132 may include a fat tableset 308. Fat tableset 308 mayrelate to a nourishment composition of an edible with respect to thequantity and/or type of esterified fatty acids in the edible. Fattableset 308 may include, without limitation, triglycerides,monoglycerides, diglycerides, phospholipids, sterols, waxes, and freefatty acids. Edible directory 132 may include a fiber tableset 312.Fiber tableset 312 may relate to a nourishment composition of an ediblewith respect to the quantity and/or type of fiber in the edible. As anon-limiting example, fiber tableset 312 may include soluble fiber, suchas beta-glucans, raw guar gum, psyllium, inulin, and the like thereof aswell as insoluble fiber, such as wheat bran, cellulose, lignin, and thelike thereof. Edible directory 132 may include a mineral tableset 316.Mineral tableset 316 may relate to a nourishment composition of anedible with respect to the quantity and/or type of minerals in theedible. As a non-limiting example, mineral tableset 316 may includecalcium, phosphorous, magnesium, sodium, potassium, chloride, sulfur,iron, manganese, copper, iodine, zing, cobalt, fluoride, selenium, andthe like thereof. Edible directory 132 may include a protein tableset320. Protein tableset 320 may relate to a nourishment composition of anedible with respect to the quantity and/or type of proteins in theedible. As a non-limiting example, protein tableset 320 may includeamino acids combinations, wherein amino acids may include, withoutlimitation, alanine, arginine, asparagine, aspartic acid, cysteine,glutamine, glutamic acid, glycine, histidine, isoleucine, leucine,lysine, methionine, phenylalanine, proline, serine, threonine,tryptophan, tyrosine, valine, and the like thereof. Edible directory 132may include a vitamin tableset 324. Vitamin tableset 324 may relate to anourishment composition of an edible with respect to the quantity and/ortype of vitamins in the edible. As a non-limiting example, vitamintableset 324 may include vitamin A, vitamin B₁, vitamin B₂, vitamin B₃,vitamin B₅, vitamin B₆, vitamin B₇, vitamin B₉, vitamin B₁₂, vitamin C,vitamin D, vitamin E, vitamin K, and the like thereof.

Now referring to FIG. 4 an exemplary embodiment 400 of biomarkers thatmay be received from respiratory volume collection 108 is illustrated.Respiratory volume collection 108 may collect a breath 404. As used inthis disclosure “breath” is air that is taken into and/or expelled fromthe lungs, wherein the air interacts with one or more alveolus of thelung. Breath 404 may include one or more chemical elements such asnitrogen, oxygen, argon, carbon dioxide, neon, helium, and/or hydrogen.As a non-limiting example, breath 404 may be comprised of 78% ofnitrogen, 20.95% of oxygen, and 1.05% of neon. As a further non-limitingexample, breath 404 may be comprised of 78% nitrogen, 16% oxygen, 1%argon, and 5% carbon dioxide. Breath 404 may contain a bioremnant 408.As used in this disclosure “bioremnant” is a biological component thatoriginates from an individual's body that represents the status of anindividual's respiratory system. As a non-limiting example bioremnant408 may include a biological component such as a Lung function,Alpha1-antitrypsin (AAT), angiogenic growth factor, brain natriureticpeptide (BNP), calprotectin, CF-specific serum proteomic signature,chromagranim A (CgA), copeptin, C-reactive protein (CRP), IgE, Nitricoxide, osteoprotegerin, parathyroid hormone, serum amyloid A, surfactantproteins, and the like thereof. Breath 404 may include a volatileorganic compound (VOC) 412. As used in this disclosure “volatile organiccompound (VOC)” is an organic compound that has a high vapor pressurethat allows the organic compound to evaporate and/or sublime at roomtemperature. VOC 412 may include one or more biologically generatedVOCs, wherein biologically generated VOCs may include, withoutlimitation, isoprene, terpenes, pinene isomers, sesquiterpenes,methanol, acetone, and the like thereof. As a non-limiting example VOC412 may include benzene, ethylene glycol, formaldehyde, methylenechloride, tetrachloroethylene, toluene, xylene, 1,3-butadiene, and thelike thereof.

Referring now to FIG. 5, an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample nourishment compositions and nourishment deficiencies may beinputs, wherein an edible is outputted.

Further referring to FIG. 5, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data to classes ofdeficiencies, wherein a nourishment deficiency may be categorized to alarge deficiency, a medium deficiency, and/or a small deficiency.

Still referring to FIG. 5, machine-learning module 500 may be configuredto perform a lazy-learning process 520 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 504. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 504elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 5,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5, machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude nourishment compositions and/or nourishment deficiencies asdescribed above as inputs, edibles as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5, machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5, machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 6, an exemplary embodiment of a method 600 forgenerating a pulmonary dysfunction nourishment program is illustrated.At step 605, a computing device 104 receives a respiratory collection108. Computing device 104 includes any of the computing device 104 asdescribed above, in reference to FIGS. 1-5. Respiratory volumecollection 108 includes any of the respiratory volume collection 108 asdescribed above, in reference to FIGS. 1-5. For instance, and withoutlimitation, respiratory volume collection 108 may include one or morebreath and/or blood samples provide by a user.

Still referring to FIG. 6, at step 610, computing device 104 generatesat least a respiratory parameter 112 of a plurality of respiratoryparameters as a function of respiratory volume collection 108.Respiratory parameter 112 includes any of the respiratory parameter 112as described above, in reference to FIGS. 1-5. Computing device 104generates respiratory parameter 112 using a respiratory algorithm 116.Respiratory algorithm 116 includes any of the respiratory algorithm 116as described above, in reference to FIGS. 1-5.

Still referring to FIG. 6, at step 615, computing device 104 determinesa pulmonary bundle element 120 as a function of respiratory parameter112. Pulmonary bundle element 120 includes any of the pulmonary bundleelement 120 as described above, in reference to FIGS. 1-5.

Still referring to FIG. 6, at step 620, computing device 104 identifiesat least an edible 124 as a function of pulmonary bundle element 120.Edible 124 includes any of the edible 124 as described above, inreference to FIGS. 1-5. Edible 124 is identified by obtaining anourishment composition 128 from an edible directory 132. Nourishmentcomposition 128 includes any of the nourishment composition 128 asdescribed above in reference to FIGS. 1-5. Edible directory 132 includesany of the edible directory 132 as described above, in reference toFIGS. 1-5. Edible 124 is identified by determining a nourishmentdeficiency 136 as a function of pulmonary bundle element 120.Nourishment deficiency 136 includes any of the nourishment deficiency136 as described above, in reference to FIGS. 1-5. Edible 124 isidentified using nourishment composition 128, nourishment deficiency136, and an edible machine-learning model 140. Edible machine-learningmodel 140 includes any of the edible machine-learning model 140 asdescribed above, in reference to FIGS. 1-5.

Still referring to FIG. 6, at step 625, computing device 104, outputs anourishment program 144 of a plurality of nourishment programs as afunction of edible 124. Nourishment program 144 includes any of thenourishment program 144 as described above, in reference to FIGS. 1-5.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating a pulmonary dysfunctionnourishment program, the system comprising: a computing device, thecomputing device configured to: receive at least a respiratory volumecollection relating to a user; generate at least a respiratory parameterof a plurality of respiratory parameters as a function of the at least arespiratory volume collection; determine a pulmonary bundle element as afunction of the at least respiratory parameter; identify at least anedible as a function of the pulmonary bundle element, wherein theidentifying comprises: obtaining a nourishment composition from anedible directory; determining a nourishment deficiency as a function ofthe pulmonary bundle element; and identifying the at least an edible asa function of the nourishment composition, the nourishment deficiency,and an edible machine-learning model; and output a nourishment programas a function of the at least an edible.
 2. The system of claim 1,wherein receiving the at least a respiratory volume collection includesobtaining a respiratory signal from at least a sensor and receiving theat least a respiratory volume collection as a function of therespiratory signal.
 3. The system of claim 1, wherein determining thepulmonary bundle element further comprises: identifying at least apulmonary deficiency as a function of the at least a respiratoryparameter and a respiratory threshold; and determining the pulmonarybundle element as a function of the at least a pulmonary deficiency. 4.The system of claim 1, wherein identifying the at least an ediblefurther comprises determining a pulmonary dysfunction as a function ofthe pulmonary bundle element and identifying the at least an edible as afunction of the pulmonary dysfunction.
 5. The system of claim 4, whereindetermining the pulmonary dysfunction further comprises: obtaining apulmonary training set, wherein the pulmonary training set relates aventilatory enumeration and a ventilatory effect; and determining thepulmonary dysfunction using the pulmonary bundle element and a pulmonarymachine-learning model, wherein the pulmonary machine-learning model istrained as a function of the pulmonary training set.
 6. The system ofclaim 1, wherein identifying the at least an edible further comprises:determining a likelihood parameter, wherein the likelihood parameterrelates a user taste profile to an edible profile; and identifying theat least an edible as a function of the likelihood parameter.
 7. Thesystem of claim 6, wherein determining the edible profile furthercomprises receiving a flavor variable from a flavor directory anddetermining the edible profile as a function of the flavor variable. 8.The system of claim 1, wherein outputting the nourishment programfurther comprises: retrieving an intended outcome; and outputting thenourishment program as a function of the intended outcome using anourishment machine-learning model.
 9. The system of claim 8, whereinthe intended outcome includes a treatment outcome.
 10. The system ofclaim 8, wherein the intended outcome includes a prevention outcome. 11.A method for generating a pulmonary dysfunction nourishment program, themethod comprising: receiving, by a computing device, at least arespiratory volume collection relating to a user; generating, by thecomputing device, at least a respiratory parameter of a plurality ofrespiratory parameters as a function of the at least a respiratoryvolume collection; determining, by the computing device, a pulmonarybundle element as a function of the at least respiratory parameter;identifying, by the computing device, at least an edible as a functionof the pulmonary bundle element, wherein the identifying comprises:obtaining a nourishment composition from an edible directory;determining a nourishment deficiency as a function of the pulmonarybundle element; and identifying the at least an edible as a function ofthe nourishment composition, the nourishment deficiency, and an ediblemachine-learning model; and outputting, by the computing device, anourishment program as a function of the at least an edible.
 12. Themethod of claim 11, wherein receiving the at least a respiratory volumecollection includes obtaining a respiratory signal from at least asensor and receiving the at least a respiratory volume collection as afunction of the respiratory signal.
 13. The method of claim 11, whereindetermining the pulmonary bundle element further comprises: identifyingat least a pulmonary deficiency as a function of the respiratoryparameter and a respiratory threshold; and determining the pulmonarybundle element as a function of the at least a pulmonary deficiency. 14.The method of claim 11, wherein identifying the at least an ediblefurther comprises determining a pulmonary dysfunction as a function ofthe pulmonary bundle element and identifying the at least an edible as afunction of the pulmonary dysfunction.
 15. The method of claim 14,wherein determining the pulmonary dysfunction further comprises:obtaining a pulmonary training set, wherein the pulmonary training setrelates a ventilatory enumeration and a ventilatory effect; anddetermining the pulmonary dysfunction using the pulmonary bundle elementand a pulmonary machine-learning model, wherein the pulmonarymachine-learning model is trained as a function of the pulmonarytraining set.
 16. The method of claim 11, wherein identifying the atleast an edible further comprises: determining a likelihood parameter,wherein the likelihood parameter relates a user taste profile to anedible profile; and identifying the at least an edible as a function ofthe likelihood parameter.
 17. The method of claim 16, whereindetermining the edible profile further comprises receiving a flavorvariable from a flavor directory and determining the edible profile as afunction of the flavor variable.
 18. The method of claim 11, whereinoutputting the nourishment program further comprises: retrieving anintended outcome; and outputting the nourishment program as a functionof the intended outcome using a nourishment machine-learning model. 19.The method of claim 18, wherein the intended outcome includes atreatment outcome.
 20. The method of claim 18, wherein the intendedoutcome includes a prevention outcome.